160 citations · 1.2k across the 55 of their papers we have counts for
7 papers · 1 filter
DUM: Diversity-Weighted Utility Maximization for Recommendations
Azin Ashkan, Branislav Kveton, Shlomo Berkovsky +1
The need for diversification of recommendation lists manifests in a number of recommender systems use cases. However, an increase in diversity may undermine the utility of the reco…
Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits
Branislav Kveton, Zheng Wen, Azin Ashkan +1
A stochastic combinatorial semi-bandit is an online learning problem where at each step a learning agent chooses a subset of ground items subject to constraints, and then observes…
Efficient Learning in Large-Scale Combinatorial Semi-Bandits
Zheng Wen, Branislav Kveton, Azin Ashkan
A stochastic combinatorial semi-bandit is an online learning problem where at each step a learning agent chooses a subset of ground items subject to combinatorial constraints, and…
Learning to Act Greedily: Polymatroid Semi-Bandits
Branislav Kveton, Zheng Wen, Azin Ashkan +1
Many important optimization problems, such as the minimum spanning tree and minimum-cost flow, can be solved optimally by a greedy method. In this work, we study a learning variant…
Matroid Bandits: Fast Combinatorial Optimization with Learning
Branislav Kveton, Zheng Wen, Azin Ashkan +2
A matroid is a notion of independence in combinatorial optimization which is closely related to computational efficiency. In particular, it is well known that the maximum of a cons…
Generalization and Exploration via Randomized Value Functions
Ian Osband, Benjamin Van Roy, Zheng Wen
We propose randomized least-squares value iteration (RLSVI) -- a new reinforcement learning algorithm designed to explore and generalize efficiently via linearly parameterized valu…
Optimal Demand Response Using Device Based Reinforcement Learning
Zheng Wen, Daniel O'Neill, Hamid Reza Maei
Demand response (DR) for residential and small commercial buildings is estimated to account for as much as 65% of the total energy savings potential of DR, and previous work shows…